parallel-mean-bottleneck-gpt2-medium-wikitext

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.1861
• Accuracy: 0.4193
• Perplexity: 24.1930
• Bleu: 0.1440

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Perplexity	Bleu
6.0438	0.2806	500	5.9200	0.1897	372.4009	0.0359
5.0422	0.5612	1000	4.8934	0.2636	133.4091	0.0610
4.3494	0.8418	1500	4.2389	0.3183	69.3337	0.0833
3.9486	1.1223	2000	3.8856	0.3521	48.6953	0.1037
3.7605	1.4029	2500	3.7143	0.3671	41.0301	0.1206
3.6544	1.6835	3000	3.5898	0.3781	36.2282	0.1332
3.5527	1.9641	3500	3.5051	0.3862	33.2836	0.1349
3.4346	2.2447	4000	3.4410	0.3919	31.2181	0.1335
3.374	2.5253	4500	3.3867	0.3972	29.5672	0.1354
3.3442	2.8058	5000	3.3410	0.4017	28.2468	0.1405
3.2251	3.0864	5500	3.3072	0.4055	27.3093	0.1404
3.2187	3.3670	6000	3.2781	0.4088	26.5242	0.1401
3.1975	3.6476	6500	3.2494	0.4118	25.7753	0.1433
3.172	3.9282	7000	3.2276	0.4142	25.2178	0.1445
3.1055	4.2088	7500	3.2109	0.4163	24.8014	0.1447
3.0676	4.4893	8000	3.1977	0.4178	24.4763	0.1453
3.0779	4.7699	8500	3.1861	0.4193	24.1930	0.1440

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0